Policy Shaping: Integrating Human Feedback with Reinforcement Learning

NeurIPS 2013 Shane GriffithKaushik SubramanianJonathan ScholzCharles L. IsbellAndrea L. Thomaz

A long term goal of Interactive Reinforcement Learning is to incorporate non-expert human feedback to solve complex tasks. State-of-the-art methods have approached this problem by mapping human information to reward and value signals to indicate preferences and then iterating over them to compute the necessary control policy... (read more)

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